Biblio

Augmented Reality (AR) is a technique which seamlessly integrate virtual 3D models into the image of the real scenario in real time. Using the QR code as the identification mark, an algorithm is proposed to extract the virtual straight line of QR code and to locate and track the camera based on the hybrid features, thus it avoids the possibility of failure when locating and tracking only by feature points. The experimental results show that the method of combining straight lines with feature points is better than that of using only straight lines or feature points. Further, an AR (Augmented Reality) system is developed.

Our previous work, which can be referred to as EMPRESS 1.0, showed that rich metadata management provides a relatively low-overhead approach to facilitating insight from scale-up scientific applications. However, this system did not provide the functionality needed for a viable production system or address whether such a system could scale. Therefore, we have extended our previous work to create EMPRESS 2.0, which incorporates the features required for a useful production system. Through a discussion of EMPRESS 2.0, this paper explores how to incorporate rich query functionality, fault tolerance, and atomic operations into a scalable, storage system independent metadata management system that is easy to use. This paper demonstrates that such a system offers significant performance advantages over HDF5, providing metadata querying that is 150X to 650X faster, and can greatly accelerate post-processing. Finally, since the current implementation of EMPRESS 2.0 relies on an RDBMS, this paper demonstrates that an RDBMS is a viable technology for managing data-oriented metadata.

Information and Communication Technologies (ICTs) present a number of vulnerabilities, threats and risks that could lead to devastating cyber-attacks resulting into huge financial losses, legal implications, and reputational damage for large and small organizations. As such, in this digital transformation and 4th industrial revolution era, nations and organizations have accepted that cybersecurity must be part of their strategic objectives and priorities. However, cybersecurity in itself is a multifaceted problem to address and the voluntary "one-size-fits-all" cybersecurity approaches have proven not effective in dealing with cyber incidents, especially in complex operational environments (e.g. large technology-centric organizations) that are multi-disciplinary, multi-departmental, multi-role, multinational, and operating across different locations. Addressing modern cybersecurity challenges requires more than a technical solution. A contextual and systematic approach that considers the complexities of these large digital environments in order to achieve resilient, sustainable, cost-effective and proactive cybersecurity is desirable. This paper aims to highlight through a single case study approach the multifaceted nature and complexity of the cybersecurity environment, pertinently in multi-disciplinary organizations. Essentially, this paper contributes a unified cybersecurity framework underpinned by an integrated capability management (ICM) approach that addresses the multifaceted nature of cybersecurity as well as the challenges and requirements eminent in complex environments, such as national government, municipalities or large corporations. The unified framework incorporates realistic and practical guidelines to bridge the gap between cybersecurity capability requirements, governance instruments and cybersecurity capability specification, implementation, employment and sustainment drawing from well-tested military capability development approaches.

We demonstrate 5G network slicing as a unique deployment opportunity for information centric networking (ICN), by using a generic service orchestration framework that operates on commodity compute, storage, and bandwidth resource pools to realize ICN service slices. In this demo, we specifically propose a service slice for the IoT Edge network. ICN has often been considered pertinent for IoT use due to its benefits like simpler stacks on resource constrained devices, in-network caching, and in-built data provenance. We use a lightweight ICN stack on IoT devices connected with sensors and actuators to build a network, where clients can set realistic policies using their legacy hand-held devices. We employ name based authentication protocols between the service end-points and IoT devices to allow secure onboarding. The IoT slice co-exists with other service slices that cater to different classes of applications (e.g., bandwidth intensive applications, such as video conferencing) allowing resource management flexibility. Our design creates orchestrated service Edge functions to which the clients connect, and these can in turn utilize in-network stateless functions to perform tasks, such as decision making and analytics using the available compute resources efficiently.

The globalization of the IC supply chain has brought forth the era of fabless companies. Due to security issues during design and fabrication processes, various security concerns have risen, ranging from IP piracy and reverse engineering to hardware Trojans. Logic encryption has emerged as a mitigation against these threats. However, no generic metrics for quantifying the security of logic encryption algorithms has been reported so far, making it impossible to formally compare different approaches. In this paper, we propose a unifying metric, capturing the key security aspects of logic encryption algorithms. The metric is evaluated on state-of-the-art algorithms and benchmarks.

The paper is devoted to analysis of condition of executing devices and sensors of Industrial Control Systems information security. The work contains structures of industrial control systems divided into groups depending on system's layer. The article contains the analysis of analog interfaces work and work features of data transmission protocols in industrial control system field layer. Questions about relevance of industrial control systems information security, both from the point of view of the information security occurring incidents, and from the point of view of regulators' reaction in the form of normative legal acts, are described. During the analysis of the information security systems of industrial control systems a possibility of leakage through technical channels of information leakage at the field layer was found. Potential vectors of the attacks on devices of field layer and data transmission network of an industrial control system are outlined in the article. The relevance analysis of the threats connected with the attacks at the field layer of an industrial control system is carried out, feature of this layer and attractiveness of this kind of attacks is observed.

This study builds a foundation for improving research for first responder communication and situational awareness technology in the future. In an online survey, we elicited the opinions of 250 U.S. first responders about effectiveness, security, and reliability of past, current, and future Internet of Things technology. The most desired features respondents identified were connectivity, reliability, interoperability, and affordability. The top barriers to technology adoption and use included restricted budgets/costs, interoperability, insufficient training resources, and insufficient interagency collaboration and communication. First responders in all job types indicated that technology has made first responder equipment more useful, and technology that supports situational awareness is particularly valued. As such, future Internet of Things capabilities, such as tapping into smart device data in residences and piggybacking onto alternative communication channels, could be valuable for future first responders. Potential areas for future investigation are suggested for technology development and research.

An important principle in computational security is to reduce the attack surface, by maintaining the Trusted Computing Base (TCB) small. Even so, no security technique ensures full protection against any adversary. Thus, sensitive applications should be designed with several layers of protection so that, even if a layer might be violated, sensitive content will not be compromised. In 2015, Intel released the Software Guard Extensions (SGX) technology in its processors. This mechanism allows applications to allocate enclaves, which are private memory regions that can hold code and data. Other applications and even privileged code, like the OS kernel and the BIOS, are not able to access enclaves' contents. This paper presents a novel password file protection scheme, which uses Intel SGX to protect authentication credentials in the PAM authentication framework, commonly used in UNIX systems. We defined and implemented an SGX-enabled version of the pam\_unix.so authentication module, called UniSGX. This module uses an SGX enclave to handle the credentials informed by the user and to check them against the password file. To add an extra security layer, the password file is stored using SGX sealing. A threat model was proposed to assess the security of the proposed solution. The obtained results show that the proposed solution is secure against the threat model considered, and that its performance overhead is acceptable from the user point of view. The scheme presented here is also suitable to other authentication frameworks.

Current BLE transmitters are susceptible to selective jamming due to long dwell times in a channel. To mitigate these attacks, we propose physical-layer security through an ultra-fast bit-level frequency-hopping (FH) scheme by exploiting the frequency agility of bulk acoustic wave resonators (BAW). Here we demonstrate the first integrated bit-level FH transmitter (TX) that hops at 1$μ$s period and uses data-driven random dynamic channel selection to enable secure wireless communications with additional data encryption. This system consists of a time-interleaved BAW-based TX implemented in 65nm CMOS technology with 80MHz coverage in the 2.4GHz ISM band and a measured power consumption of 10.9mW from 1.1V supply.

With the frequent use of Wi-Fi and hotspots that provide a wireless Internet environment, awareness and threats to wireless AP (Access Point) security are steadily increasing. Especially when using unauthorized APs in company, government and military facilities, there is a high possibility of being subjected to various viruses and hacking attacks. It is necessary to detect unauthorized Aps for protection of information. In this paper, we use RTT (Round Trip Time) value data set to detect authorized and unauthorized APs in wired / wireless integrated environment, analyze them using machine learning algorithms including SVM (Support Vector Machine), C4.5, KNN (K Nearest Neighbors) and MLP (Multilayer Perceptron). Overall, KNN shows the highest accuracy.

In practice, Defenders need a more efficient network detection approach which has the advantages of quick-responding learning capability of new network behavioural features for network intrusion detection purpose. In many applications the capability of Deep Learning techniques has been confirmed to outperform classic approaches. Accordingly, this study focused on network intrusion detection using convolutional neural networks (CNNs) based on LeNet-5 to classify the network threats. The experiment results show that the prediction accuracy of intrusion detection goes up to 99.65% with samples more than 10,000. The overall accuracy rate is 97.53%.

In recent years, new and devastating cyber attacks amplify the need for robust cybersecurity practices. Preventing novel cyber attacks requires the invention of Intrusion Detection Systems (IDSs), which can identify previously unseen attacks. Many researchers have attempted to produce anomaly - based IDSs, however they are not yet able to detect malicious network traffic consistently enough to warrant implementation in real networks. Obviously, it remains a challenge for the security community to produce IDSs that are suitable for implementation in the real world. In this paper, we propose a new approach using a Deep Belief Network with a combination of supervised and unsupervised machine learning methods for port scanning attacks detection - the task of probing enterprise networks or Internet wide services, searching for vulnerabilities or ways to infiltrate IT assets. Our proposed approach will be tested with network security datasets and compared with previously existing methods.

As mobile devices and location-based services become increasingly ubiquitous, the privacy of mobile users' location traces continues to be a major concern. Traditional privacy solutions rely on perturbing each position in a user's trace and replacing it with a fake location. However, recent studies have shown that such point-based perturbation of locations is susceptible to inference attacks and suffers from serious utility losses, because it disregards the moving trajectory and continuity in full location traces. In this paper, we argue that privacy-preserving synthesis of complete location traces can be an effective solution to this problem. We present AdaTrace, a scalable location trace synthesizer with three novel features: provable statistical privacy, deterministic attack resilience, and strong utility preservation. AdaTrace builds a generative model from a given set of real traces through a four-phase synthesis process consisting of feature extraction, synopsis learning, privacy and utility preserving noise injection, and generation of differentially private synthetic location traces. The output traces crafted by AdaTrace preserve utility-critical information existing in real traces, and are robust against known location trace attacks. We validate the effectiveness of AdaTrace by comparing it with three state of the art approaches (ngram, DPT, and SGLT) using real location trace datasets (Geolife and Taxi) as well as a simulated dataset of 50,000 vehicles in Oldenburg, Germany. AdaTrace offers up to 3-fold improvement in trajectory utility, and is orders of magnitude faster than previous work, while preserving differential privacy and attack resilience.

High-level C++ source code abstractions such as classes and methods greatly assist human analysts and automated algorithms alike when analyzing C++ programs. Unfortunately, these abstractions are lost when compiling C++ source code, which impedes the understanding of C++ executables. In this paper, we propose a system, OOAnalyzer, that uses an innovative new design to statically recover detailed C++ abstractions from executables in a scalable manner. OOAnalyzer's design is motivated by the observation that many human analysts reason about C++ programs by recognizing simple patterns in binary code and then combining these findings using logical inference, domain knowledge, and intuition. We codify this approach by combining a lightweight symbolic analysis with a flexible Prolog-based reasoning system. Unlike most existing work, OOAnalyzer is able to recover both polymorphic and non-polymorphic C++ classes. We show in our evaluation that OOAnalyzer assigns over 78% of methods to the correct class on our test corpus, which includes both malware and real-world software such as Firefox and MySQL. These recovered abstractions can help analysts understand the behavior of C++ malware and cleanware, and can also improve the precision of program analyses on C++ executables.

With the rapid growth of the cyber attacks, cyber threat intelligence (CTI) sharing becomes essential for providing advance threat notice and enabling timely response to cyber attacks. Our goal in this paper is to develop an approach to extract low-level cyber threat actions from publicly available CTI sources in an automated manner to enable timely defense decision making. Specifically, we innovatively and successfully used the metrics of entropy and mutual information from Information Theory to analyze the text in the cybersecurity domain. Combined with some basic NLP techniques, our framework, called ActionMiner has achieved higher precision and recall than the state-of-the-art Stanford typed dependency parser, which usually works well in general English but not cybersecurity texts.

Immersive technologies have been touted as empathetic mediums. This capability has yet to be fully explored through machine learning integration. Our demo seeks to explore proxemics in mixed-reality (MR) human-human interactions. The author developed a system, where spatial features can be manipulated in real time by identifying emotions corresponding to unique combinations of facial micro-expressions and tonal analysis. The Magic Leap One is used as the interactive interface, the first commercial spatial computing head mounted (virtual retinal) display (HUD). A novel spatial user interface visualization element is prototyped that leverages the affordances of mixed-reality by introducing both a spatial and affective component to interfaces.

In the management of transport and logistics, which includes the delivery, movement and collection of goods through roads, ports and airports, participate, in general, many different actors. The most critical aspects of supply chain systems include time, space and interdependencies. Besides, there are several security challenges that can be caused both by unintentional and intentional errors. With all this in mind, this work proposes the combination of technologies such as RFID, GPS, WiFi Direct and LTE/3G to automate product authentication and merchandise tracking, reducing the negative effects caused either by mismanagement or attacks against the process of the supply chain. In this way, this work proposes a ubiquitous management scheme for the monitoring through the cloud of freight and logistics systems, including demand management, customization and automatic replenishment of out-of-stock goods. The proposal implies an improvement in the efficiency of the systems, which can be quantified in a reduction of time and cost in the inventory and distribution processes, and in a greater facility for the detection of counterfeit versions of branded articles. In addition, it can be used to create safer and more efficient schemes that help companies and organizations to improve the quality of the service and the traceability of the transported goods.

Program analyses necessarily make approximations that often lead them to report true alarms interspersed with many false alarms. We propose a new approach to leverage user feedback to guide program analyses towards true alarms and away from false alarms. Our approach associates each alarm with a confidence value by performing Bayesian inference on a probabilistic model derived from the analysis rules. In each iteration, the user inspects the alarm with the highest confidence and labels its ground truth, and the approach recomputes the confidences of the remaining alarms given this feedback. It thereby maximizes the return on the effort by the user in inspecting each alarm. We have implemented our approach in a tool named Bingo for program analyses expressed in Datalog. Experiments with real users and two sophisticated analyses–-a static datarace analysis for Java programs and a static taint analysis for Android apps–-show significant improvements on a range of metrics, including false alarm rates and number of bugs found.

Binary particle swarm optimization (BPSO) is a technique widely used to solve combinatorial problems. In this paper, we propose a variant of BPSO to find most likely attack paths in an attack graph. The aim is to find an attack path with the highest attack probability and least path length. In such combinatorial optimization problem, the set of feasible solutions is usually discrete and an exhaustive search may lead to unnecessary examination of those segments of the search space, which are assured to not include a solution. The paper introduces the concept of bounding the solution space of BPSO. The minimum and maximum value of each objective called bound of the solution is computed. The search space of BPSO is restricted within these solution bounds and hence we name our approach as bounded binary particle swarm optimization (BBPSO). By bounding the solution space, those particles of BPSO which are guaranteed to be infeasible are not considered for feasibility check. Experimental results show that the proposed approach provide a 50 percent performance improvement as compared to the conventional BPSO.

Deprecation is a language feature that allows API producers to mark a feature as obsolete. We aim to gain a deep understanding of the needs of API producers and consumers alike regarding deprecation. To that end, we investigate why API producers deprecate features, whether they remove deprecated features, how they expect consumers to react, and what prompts an API consumer to react to deprecation. To achieve this goal we conduct semi-structured interviews with 17 third-party Java API producers and survey 170 Java developers. We observe that the current deprecation mechanism in Java and the proposal to enhance it does not address all the needs of a developer. This leads us to propose and evaluate three further enhancements to the deprecation mechanism.

The possibility of anonymity and lack of effective ways to identify inappropriate messages have resulted in a significant amount of online interaction data that attempt to harass, bully, or offend the recipient. In this work, we perform a preliminary linguistic study on messages exchanged using one such popular web/smartphone application—Sarahah, that allows friends to exchange messages anonymously. Since messages exchanged via Sarahah are private, we collect them when the recipient shares it on Twitter. We then perform an analysis of the different kinds of messages exchanged through this application. Our linguistic analysis reveals that a significant number of these messages ($\backslash$textasciitilde20%) include inappropriate, hurtful, or profane language intended to embarrass, offend, or bully the recipient. Our analysis helps in understanding the different ways in which anonymous message exchange platforms are used and the different types of bullying present in such exchanges.

Multi-Objective Recommender Systems (MO-RS) consider several objectives to produce useful recommendations. Besides accuracy, other important quality metrics include novelty and diversity of recommended lists of items. Previous research up to this point focused on naive combinations of objectives. In this paper, we present a new and adaptable strategy for prioritizing objectives focused on users' preferences. Our proposed strategy is based on meta-features, i.e., characteristics of the input data that are influential in the final recommendation. We conducted a series of experiments on three real-world datasets, from which we show that: (i) the use of meta-features leads to the improvement of the Pareto solution set in the search process; (ii) the strategy is effective at making choices according to the specificities of the users' preferences; and (iii) our approach outperforms state-of-the-art methods in MO-RS.

Web security is a big concern in the current Internet; users may visit websites that automatically download malicious codes for leaking user's privacy information, or even mildly their web browser may help for someone's cryptomining. In this paper, we analyze abusive web resources (i.e. malicious resources and cryptomining) crawled from the Alexa Top 150,000 sites. We highlight the abusive web resources on Alexa ranking, TLD usage, website geolocation, and domain lifetime. Our results show that abusive resources are spread in the Alexa ranking, websites particularly generic Top Level Domain (TLD) and their recently registered domains. In addition, websites with malicious resources are mainly located in China while cryptomining is located in USA. We further evaluate possible counter-measures against abusive web resources. We observe that ad or privacy block lists are ineffective to block against malicious resources while coin-blocking lists are powerful enough to mitigate in-browser cryptomining. Our observations shed light on a little studied, yet important, aspect of abusive resources, and can help increase user awareness about the malicious resources and drive-by mining on web browsers.

Wireless mesh networking (WMN) is a new technology aimed to introduce the benefits of using multi-hop and multi-path to the wireless world. However, the absence of a fast and reliable handoff protocol is a major drawback especially in a technology designed to feature high mobility and scalability. We propose a fast and efficient handoff authentication protocol for wireless mesh networks. It is a token-based authentication protocol using pre-distributed parameters. We provide a performance comparison among our protocol, UFAP, and other protocols including EAP-TLS and EAP-PEAP tested in an actual setup. Performance analysis will prove that our proposed handoff authentication protocol is 250 times faster than EAP-PEAP and 500 times faster than EAP-TLS. The significant improvement in performance allows UFAP to provide seamless handoff and continuous operation even for real-time applications which can only tolerate short delays under 50 ms.

We present a new method to protect projected content from secret photography using high-speed projection. Protection techniques for digital copies have been discussed over many years from the viewpoint of data protection. However, content displayed by general display techniques is not only visible to the human eye but also can be captured by cameras. Therefore, projected content is, at times, secretly taken by malicious small cameras even when protection techniques for digital copies are adopted. In this study, we aim to realize a protectable projection method that allows people to observe content with their eyes but not record content with camera devices.